getuniversal.link: A Tool for Testing Apple App Site Association Files

Hacker News

I'm excited to announce the launch of getuniversal.link, a new utility designed to simplify the testing of your Apple App Site Association (AASA) file configuration. With getuniversal.link, you can: - Verify the configuration of your AASA file effortlessly. - Easily create test links for you and your team. - Save the website as a bookmark on your device or the iOS Simulator for convenient access and testing. I hope this tool will be helpful for developers looking to streamline their workflow and ensure their AASA files are correctly set up. Check it out at getuniversal.link and let me know your feedback! Happy testing! -Aryaman S.

Visit product View on Hacker NewsMay 26, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, new · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

18%18% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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